Overview

Dataset statistics

Number of variables264
Number of observations3601
Missing cells0
Missing cells (%)0.0%
Duplicate rows442
Duplicate rows (%)12.3%
Total size in memory7.3 MiB
Average record size in memory2.1 KiB

Variable types

Numeric3
Categorical261

Alerts

Dataset has 442 (12.3%) duplicate rowsDuplicates
0 is highly overall correlated with 1 and 5 other fieldsHigh correlation
1 is highly overall correlated with 0High correlation
123 is highly overall correlated with 0High correlation
124 is highly overall correlated with 226High correlation
127 is highly overall correlated with 0High correlation
146 is highly overall correlated with 243High correlation
166 is highly overall correlated with 254High correlation
226 is highly overall correlated with 124High correlation
232 is highly overall correlated with 0High correlation
233 is highly overall correlated with 42High correlation
235 is highly overall correlated with 51High correlation
237 is highly overall correlated with 253High correlation
243 is highly overall correlated with 146High correlation
245 is highly overall correlated with 0High correlation
247 is highly overall correlated with 0High correlation
252 is highly overall correlated with 81High correlation
253 is highly overall correlated with 237 and 1 other fieldsHigh correlation
254 is highly overall correlated with 166High correlation
256 is highly overall correlated with 56High correlation
258 is highly overall correlated with 32High correlation
263 is highly overall correlated with 253High correlation
32 is highly overall correlated with 258High correlation
42 is highly overall correlated with 233High correlation
51 is highly overall correlated with 235High correlation
56 is highly overall correlated with 256High correlation
81 is highly overall correlated with 252High correlation
3 is highly imbalanced (99.3%)Imbalance
4 is highly imbalanced (99.3%)Imbalance
5 is highly imbalanced (99.3%)Imbalance
6 is highly imbalanced (99.6%)Imbalance
7 is highly imbalanced (99.6%)Imbalance
8 is highly imbalanced (97.7%)Imbalance
9 is highly imbalanced (98.0%)Imbalance
10 is highly imbalanced (99.6%)Imbalance
11 is highly imbalanced (99.3%)Imbalance
12 is highly imbalanced (99.0%)Imbalance
13 is highly imbalanced (99.6%)Imbalance
14 is highly imbalanced (97.2%)Imbalance
15 is highly imbalanced (99.6%)Imbalance
16 is highly imbalanced (98.2%)Imbalance
17 is highly imbalanced (99.3%)Imbalance
18 is highly imbalanced (99.3%)Imbalance
19 is highly imbalanced (99.6%)Imbalance
20 is highly imbalanced (99.6%)Imbalance
21 is highly imbalanced (98.7%)Imbalance
22 is highly imbalanced (98.7%)Imbalance
23 is highly imbalanced (99.6%)Imbalance
24 is highly imbalanced (99.3%)Imbalance
25 is highly imbalanced (98.2%)Imbalance
26 is highly imbalanced (99.0%)Imbalance
27 is highly imbalanced (99.3%)Imbalance
28 is highly imbalanced (99.6%)Imbalance
29 is highly imbalanced (97.5%)Imbalance
30 is highly imbalanced (94.6%)Imbalance
31 is highly imbalanced (98.2%)Imbalance
32 is highly imbalanced (99.0%)Imbalance
33 is highly imbalanced (99.6%)Imbalance
34 is highly imbalanced (99.6%)Imbalance
35 is highly imbalanced (99.6%)Imbalance
36 is highly imbalanced (99.6%)Imbalance
37 is highly imbalanced (84.6%)Imbalance
38 is highly imbalanced (99.6%)Imbalance
39 is highly imbalanced (89.3%)Imbalance
40 is highly imbalanced (99.6%)Imbalance
41 is highly imbalanced (99.6%)Imbalance
42 is highly imbalanced (93.2%)Imbalance
43 is highly imbalanced (92.9%)Imbalance
44 is highly imbalanced (99.6%)Imbalance
45 is highly imbalanced (99.0%)Imbalance
46 is highly imbalanced (99.6%)Imbalance
47 is highly imbalanced (99.6%)Imbalance
48 is highly imbalanced (97.7%)Imbalance
49 is highly imbalanced (99.3%)Imbalance
50 is highly imbalanced (98.2%)Imbalance
51 is highly imbalanced (99.3%)Imbalance
52 is highly imbalanced (99.6%)Imbalance
53 is highly imbalanced (99.6%)Imbalance
54 is highly imbalanced (99.6%)Imbalance
55 is highly imbalanced (96.1%)Imbalance
56 is highly imbalanced (97.0%)Imbalance
57 is highly imbalanced (95.7%)Imbalance
58 is highly imbalanced (98.5%)Imbalance
59 is highly imbalanced (99.6%)Imbalance
60 is highly imbalanced (85.5%)Imbalance
61 is highly imbalanced (97.5%)Imbalance
62 is highly imbalanced (81.7%)Imbalance
63 is highly imbalanced (99.6%)Imbalance
64 is highly imbalanced (99.6%)Imbalance
65 is highly imbalanced (94.6%)Imbalance
66 is highly imbalanced (99.6%)Imbalance
67 is highly imbalanced (99.6%)Imbalance
68 is highly imbalanced (98.5%)Imbalance
69 is highly imbalanced (94.4%)Imbalance
70 is highly imbalanced (94.2%)Imbalance
71 is highly imbalanced (99.6%)Imbalance
72 is highly imbalanced (99.6%)Imbalance
73 is highly imbalanced (98.0%)Imbalance
74 is highly imbalanced (99.6%)Imbalance
75 is highly imbalanced (97.2%)Imbalance
76 is highly imbalanced (84.0%)Imbalance
77 is highly imbalanced (99.3%)Imbalance
78 is highly imbalanced (99.0%)Imbalance
79 is highly imbalanced (90.8%)Imbalance
80 is highly imbalanced (91.4%)Imbalance
81 is highly imbalanced (75.6%)Imbalance
82 is highly imbalanced (99.6%)Imbalance
83 is highly imbalanced (98.2%)Imbalance
84 is highly imbalanced (88.1%)Imbalance
85 is highly imbalanced (97.5%)Imbalance
86 is highly imbalanced (97.0%)Imbalance
87 is highly imbalanced (96.6%)Imbalance
88 is highly imbalanced (92.5%)Imbalance
89 is highly imbalanced (93.8%)Imbalance
90 is highly imbalanced (97.7%)Imbalance
91 is highly imbalanced (99.3%)Imbalance
92 is highly imbalanced (99.0%)Imbalance
93 is highly imbalanced (99.6%)Imbalance
94 is highly imbalanced (99.3%)Imbalance
95 is highly imbalanced (99.6%)Imbalance
96 is highly imbalanced (98.7%)Imbalance
97 is highly imbalanced (99.3%)Imbalance
98 is highly imbalanced (82.1%)Imbalance
99 is highly imbalanced (83.0%)Imbalance
100 is highly imbalanced (99.6%)Imbalance
101 is highly imbalanced (99.6%)Imbalance
102 is highly imbalanced (93.0%)Imbalance
103 is highly imbalanced (99.6%)Imbalance
104 is highly imbalanced (99.6%)Imbalance
105 is highly imbalanced (90.7%)Imbalance
106 is highly imbalanced (98.5%)Imbalance
107 is highly imbalanced (98.2%)Imbalance
108 is highly imbalanced (97.5%)Imbalance
109 is highly imbalanced (98.5%)Imbalance
110 is highly imbalanced (99.3%)Imbalance
111 is highly imbalanced (99.6%)Imbalance
112 is highly imbalanced (99.0%)Imbalance
113 is highly imbalanced (99.6%)Imbalance
114 is highly imbalanced (98.2%)Imbalance
115 is highly imbalanced (99.6%)Imbalance
116 is highly imbalanced (99.3%)Imbalance
117 is highly imbalanced (99.6%)Imbalance
118 is highly imbalanced (93.2%)Imbalance
119 is highly imbalanced (91.2%)Imbalance
120 is highly imbalanced (98.0%)Imbalance
121 is highly imbalanced (99.0%)Imbalance
122 is highly imbalanced (99.3%)Imbalance
123 is highly imbalanced (99.6%)Imbalance
124 is highly imbalanced (97.7%)Imbalance
125 is highly imbalanced (99.6%)Imbalance
126 is highly imbalanced (99.0%)Imbalance
127 is highly imbalanced (99.6%)Imbalance
128 is highly imbalanced (98.5%)Imbalance
129 is highly imbalanced (99.6%)Imbalance
130 is highly imbalanced (97.7%)Imbalance
131 is highly imbalanced (99.3%)Imbalance
132 is highly imbalanced (99.6%)Imbalance
133 is highly imbalanced (99.6%)Imbalance
134 is highly imbalanced (98.5%)Imbalance
135 is highly imbalanced (92.3%)Imbalance
136 is highly imbalanced (99.6%)Imbalance
137 is highly imbalanced (99.0%)Imbalance
138 is highly imbalanced (98.2%)Imbalance
139 is highly imbalanced (99.3%)Imbalance
140 is highly imbalanced (99.6%)Imbalance
141 is highly imbalanced (98.2%)Imbalance
142 is highly imbalanced (99.6%)Imbalance
143 is highly imbalanced (99.6%)Imbalance
144 is highly imbalanced (95.5%)Imbalance
145 is highly imbalanced (99.6%)Imbalance
146 is highly imbalanced (99.3%)Imbalance
147 is highly imbalanced (96.1%)Imbalance
148 is highly imbalanced (96.6%)Imbalance
149 is highly imbalanced (98.7%)Imbalance
150 is highly imbalanced (99.0%)Imbalance
152 is highly imbalanced (93.2%)Imbalance
153 is highly imbalanced (99.6%)Imbalance
154 is highly imbalanced (99.6%)Imbalance
155 is highly imbalanced (99.3%)Imbalance
156 is highly imbalanced (99.3%)Imbalance
157 is highly imbalanced (89.8%)Imbalance
158 is highly imbalanced (84.3%)Imbalance
159 is highly imbalanced (96.8%)Imbalance
160 is highly imbalanced (99.6%)Imbalance
161 is highly imbalanced (98.7%)Imbalance
162 is highly imbalanced (99.3%)Imbalance
163 is highly imbalanced (97.2%)Imbalance
164 is highly imbalanced (84.8%)Imbalance
165 is highly imbalanced (99.3%)Imbalance
166 is highly imbalanced (98.7%)Imbalance
167 is highly imbalanced (99.6%)Imbalance
168 is highly imbalanced (99.3%)Imbalance
169 is highly imbalanced (92.1%)Imbalance
170 is highly imbalanced (96.1%)Imbalance
171 is highly imbalanced (99.6%)Imbalance
172 is highly imbalanced (99.6%)Imbalance
173 is highly imbalanced (99.3%)Imbalance
174 is highly imbalanced (99.3%)Imbalance
175 is highly imbalanced (94.6%)Imbalance
176 is highly imbalanced (99.3%)Imbalance
177 is highly imbalanced (99.6%)Imbalance
178 is highly imbalanced (99.3%)Imbalance
179 is highly imbalanced (99.6%)Imbalance
180 is highly imbalanced (99.3%)Imbalance
181 is highly imbalanced (99.6%)Imbalance
182 is highly imbalanced (97.0%)Imbalance
183 is highly imbalanced (99.3%)Imbalance
184 is highly imbalanced (96.6%)Imbalance
185 is highly imbalanced (99.6%)Imbalance
186 is highly imbalanced (99.6%)Imbalance
187 is highly imbalanced (83.7%)Imbalance
188 is highly imbalanced (99.6%)Imbalance
189 is highly imbalanced (99.3%)Imbalance
190 is highly imbalanced (99.6%)Imbalance
191 is highly imbalanced (98.0%)Imbalance
192 is highly imbalanced (99.6%)Imbalance
193 is highly imbalanced (99.6%)Imbalance
194 is highly imbalanced (99.3%)Imbalance
195 is highly imbalanced (99.0%)Imbalance
196 is highly imbalanced (99.6%)Imbalance
197 is highly imbalanced (99.6%)Imbalance
198 is highly imbalanced (99.3%)Imbalance
199 is highly imbalanced (99.0%)Imbalance
200 is highly imbalanced (95.0%)Imbalance
201 is highly imbalanced (98.2%)Imbalance
202 is highly imbalanced (98.7%)Imbalance
203 is highly imbalanced (94.6%)Imbalance
204 is highly imbalanced (89.1%)Imbalance
205 is highly imbalanced (99.0%)Imbalance
206 is highly imbalanced (71.1%)Imbalance
207 is highly imbalanced (89.6%)Imbalance
208 is highly imbalanced (98.0%)Imbalance
209 is highly imbalanced (91.0%)Imbalance
210 is highly imbalanced (89.1%)Imbalance
211 is highly imbalanced (99.6%)Imbalance
212 is highly imbalanced (99.6%)Imbalance
213 is highly imbalanced (85.2%)Imbalance
214 is highly imbalanced (94.8%)Imbalance
215 is highly imbalanced (99.6%)Imbalance
216 is highly imbalanced (99.3%)Imbalance
217 is highly imbalanced (98.7%)Imbalance
218 is highly imbalanced (99.0%)Imbalance
219 is highly imbalanced (99.0%)Imbalance
220 is highly imbalanced (94.8%)Imbalance
221 is highly imbalanced (99.0%)Imbalance
222 is highly imbalanced (99.6%)Imbalance
223 is highly imbalanced (99.3%)Imbalance
224 is highly imbalanced (99.3%)Imbalance
225 is highly imbalanced (99.6%)Imbalance
226 is highly imbalanced (95.9%)Imbalance
227 is highly imbalanced (97.2%)Imbalance
228 is highly imbalanced (99.6%)Imbalance
229 is highly imbalanced (99.3%)Imbalance
230 is highly imbalanced (99.6%)Imbalance
231 is highly imbalanced (97.5%)Imbalance
232 is highly imbalanced (99.6%)Imbalance
233 is highly imbalanced (83.6%)Imbalance
234 is highly imbalanced (99.6%)Imbalance
235 is highly imbalanced (99.3%)Imbalance
236 is highly imbalanced (78.2%)Imbalance
238 is highly imbalanced (99.6%)Imbalance
239 is highly imbalanced (99.3%)Imbalance
240 is highly imbalanced (98.7%)Imbalance
241 is highly imbalanced (99.6%)Imbalance
242 is highly imbalanced (99.6%)Imbalance
243 is highly imbalanced (99.3%)Imbalance
244 is highly imbalanced (95.2%)Imbalance
245 is highly imbalanced (99.6%)Imbalance
246 is highly imbalanced (96.8%)Imbalance
247 is highly imbalanced (99.3%)Imbalance
248 is highly imbalanced (99.6%)Imbalance
249 is highly imbalanced (89.1%)Imbalance
250 is highly imbalanced (99.3%)Imbalance
251 is highly imbalanced (99.3%)Imbalance
252 is highly imbalanced (66.2%)Imbalance
254 is highly imbalanced (97.5%)Imbalance
255 is highly imbalanced (99.6%)Imbalance
256 is highly imbalanced (94.0%)Imbalance
257 is highly imbalanced (99.6%)Imbalance
258 is highly imbalanced (99.0%)Imbalance
259 is highly imbalanced (99.6%)Imbalance
260 is highly imbalanced (99.6%)Imbalance
261 is highly imbalanced (95.5%)Imbalance
262 is highly imbalanced (95.2%)Imbalance
0 is highly skewed (γ1 = 41.92996044)Skewed

Reproduction

Analysis started2025-12-31 00:44:13.617554
Analysis finished2025-12-31 00:46:13.304161
Duration1 minute and 59.69 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

0
Real number (ℝ)

High correlation  Skewed 

Distinct483
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9463634 × 10-18
Minimum-0.048714817
Maximum47.176633
Zeros0
Zeros (%)0.0%
Negative3391
Negative (%)94.2%
Memory size28.3 KiB
2025-12-31T03:46:13.405427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.048714817
5-th percentile-0.042815838
Q1-0.039784417
median-0.035032462
Q3-0.026102062
95-th percentile0.0024096738
Maximum47.176633
Range47.225347
Interquartile range (IQR)0.013682356

Descriptive statistics

Standard deviation1.0001389
Coefficient of variation (CV)2.5343304 × 1017
Kurtosis1826.4191
Mean3.9463634 × 10-18
Median Absolute Deviation (MAD)0.0054073981
Skewness41.92996
Sum3.7721562 × 10-15
Variance1.0002778
MonotonicityNot monotonic
2025-12-31T03:46:13.529240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03429508907151
 
4.2%
-0.0261020616564
 
1.8%
-0.0397844174554
 
1.5%
-0.0401121385553
 
1.5%
-0.0251188983552
 
1.4%
-0.0236441534250
 
1.4%
-0.0400302082747
 
1.3%
-0.0383916027940
 
1.1%
-0.03994827840
 
1.1%
-0.0408495110139
 
1.1%
Other values (473)3011
83.6%
ValueCountFrequency (%)
-0.048714817343
0.1%
-0.047076211862
 
0.1%
-0.046912351311
 
< 0.1%
-0.045847257741
 
< 0.1%
-0.045765327471
 
< 0.1%
-0.045601466921
 
< 0.1%
-0.045109885281
 
< 0.1%
-0.0450279556
0.2%
-0.044946024732
 
0.1%
-0.044864094457
0.2%
ValueCountFrequency (%)
47.176632641
 
< 0.1%
35.6914011
 
< 0.1%
6.2748276111
 
< 0.1%
4.0458325693
0.1%
2.4942370351
 
< 0.1%
0.76862159872
0.1%
0.5297129191
 
< 0.1%
0.42893868161
 
< 0.1%
0.35897022741
 
< 0.1%
0.3077638061
 
< 0.1%

1
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7293134 × 10-16
Minimum-2.6097815
Maximum2.2112632
Zeros0
Zeros (%)0.0%
Negative2104
Negative (%)58.4%
Memory size28.3 KiB
2025-12-31T03:46:13.591662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.6097815
5-th percentile-1.806274
Q1-1.0027666
median-0.19925914
Q30.60424831
95-th percentile1.4077558
Maximum2.2112632
Range4.8210447
Interquartile range (IQR)1.6070149

Descriptive statistics

Standard deviation1.0001389
Coefficient of variation (CV)2.6818311 × 1015
Kurtosis-0.56188603
Mean3.7293134 × 10-16
Median Absolute Deviation (MAD)0.80350745
Skewness0.22318741
Sum1.3429258 × 10-12
Variance1.0002778
MonotonicityNot monotonic
2025-12-31T03:46:13.639910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.1992591364958
26.6%
-1.002766583954
26.5%
0.6042483106897
24.9%
1.407755758455
12.6%
-1.80627403181
 
5.0%
2.211263205145
 
4.0%
-2.60978147711
 
0.3%
ValueCountFrequency (%)
-2.60978147711
 
0.3%
-1.80627403181
 
5.0%
-1.002766583954
26.5%
-0.1992591364958
26.6%
0.6042483106897
24.9%
1.407755758455
12.6%
2.211263205145
 
4.0%
ValueCountFrequency (%)
2.211263205145
 
4.0%
1.407755758455
12.6%
0.6042483106897
24.9%
-0.1992591364958
26.6%
-1.002766583954
26.5%
-1.80627403181
 
5.0%
-2.60978147711
 
0.3%

2
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.3086226 × 10-16
Minimum-2.5243681
Maximum4.1294684
Zeros0
Zeros (%)0.0%
Negative2742
Negative (%)76.1%
Memory size28.3 KiB
2025-12-31T03:46:13.684561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.5243681
5-th percentile-1.4153954
Q1-0.30642263
median-0.30642263
Q3-0.30642263
95-th percentile1.9115229
Maximum4.1294684
Range6.6538365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0001389
Coefficient of variation (CV)-4.3321887 × 1015
Kurtosis2.0571797
Mean-2.3086226 × 10-16
Median Absolute Deviation (MAD)0
Skewness0.78122753
Sum-8.31335 × 10-13
Variance1.0002778
MonotonicityNot monotonic
2025-12-31T03:46:13.733470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.30642262962455
68.2%
1.911522876437
 
12.1%
0.8025501233386
 
10.7%
-1.415395383181
 
5.0%
-2.524368135106
 
2.9%
4.12946838220
 
0.6%
3.02049562916
 
0.4%
ValueCountFrequency (%)
-2.524368135106
 
2.9%
-1.415395383181
 
5.0%
-0.30642262962455
68.2%
0.8025501233386
 
10.7%
1.911522876437
 
12.1%
3.02049562916
 
0.4%
4.12946838220
 
0.6%
ValueCountFrequency (%)
4.12946838220
 
0.6%
3.02049562916
 
0.4%
1.911522876437
 
12.1%
0.8025501233386
 
10.7%
-0.30642262962455
68.2%
-1.415395383181
 
5.0%
-2.524368135106
 
2.9%

3
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:13.795708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:13.849312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

4
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:13.900245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:13.943664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

5
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:13.997729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.041874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

6
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:14.097334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.140934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

7
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:14.193861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.236921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

8
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3593 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Length

2025-12-31T03:46:14.288649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.331132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

9
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3594 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Length

2025-12-31T03:46:14.382546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.429605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

10
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:14.496421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.578808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

11
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:14.650735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.693637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

12
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:14.744169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.784357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

13
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:14.833983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.876602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

14
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3591 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Length

2025-12-31T03:46:14.931165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:14.977623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

15
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:15.031262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.072129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

16
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:15.422000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.465604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

17
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:15.517043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.557395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

18
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:15.606259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.647149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

19
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:15.695588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.738078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

20
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:15.793937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.838391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

21
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:15.892185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:15.938292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

22
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:15.992782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.034880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

23
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:16.085789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.129235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

24
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:16.182751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.229486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

25
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:16.282587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.326362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

26
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:16.377998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.419680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

27
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:16.476042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.521245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

28
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:16.573730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.614759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

29
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:16.662908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.703701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

30
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3579 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Length

2025-12-31T03:46:16.752604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.792120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

31
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:16.840369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.879732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

32
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:16.929309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:16.970216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

33
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.023443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.067322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

34
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.117997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.160570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

35
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.211178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.254003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

36
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.306706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.350895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

37
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3521 
1.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03521
97.8%
1.080
 
2.2%

Length

2025-12-31T03:46:17.407972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.450395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03521
97.8%
1.080
 
2.2%

Most occurring characters

ValueCountFrequency (%)
07122
65.9%
.3601
33.3%
180
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07122
65.9%
.3601
33.3%
180
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07122
65.9%
.3601
33.3%
180
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07122
65.9%
.3601
33.3%
180
 
0.7%

38
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.502406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.543867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

39
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3550 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03550
98.6%
1.051
 
1.4%

Length

2025-12-31T03:46:17.592302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.632793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03550
98.6%
1.051
 
1.4%

Most occurring characters

ValueCountFrequency (%)
07151
66.2%
.3601
33.3%
151
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07151
66.2%
.3601
33.3%
151
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07151
66.2%
.3601
33.3%
151
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07151
66.2%
.3601
33.3%
151
 
0.5%

40
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.682359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.722762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

41
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:17.773633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.813957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

42
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3572 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Length

2025-12-31T03:46:17.863126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.903918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Most occurring characters

ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

43
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3570 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03570
99.1%
1.031
 
0.9%

Length

2025-12-31T03:46:17.953004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:17.995450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03570
99.1%
1.031
 
0.9%

Most occurring characters

ValueCountFrequency (%)
07171
66.4%
.3601
33.3%
131
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07171
66.4%
.3601
33.3%
131
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07171
66.4%
.3601
33.3%
131
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07171
66.4%
.3601
33.3%
131
 
0.3%

44
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:18.047361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.088424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

45
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:18.138696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.178935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

46
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:18.228903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.270581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

47
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:18.319728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.361110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

48
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3593 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Length

2025-12-31T03:46:18.409946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.450234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

49
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:18.500498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.542188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

50
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:18.591344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:18.986430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

51
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:19.040013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.084773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

52
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:19.134941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.177269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

53
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:19.230406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.274117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

54
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:19.325218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.365571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

55
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3586 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Length

2025-12-31T03:46:19.420533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.461400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

56
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3590 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Length

2025-12-31T03:46:19.512191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.557050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

57
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3584 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03584
99.5%
1.017
 
0.5%

Length

2025-12-31T03:46:19.608896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.650117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03584
99.5%
1.017
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07185
66.5%
.3601
33.3%
117
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07185
66.5%
.3601
33.3%
117
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07185
66.5%
.3601
33.3%
117
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07185
66.5%
.3601
33.3%
117
 
0.2%

58
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:19.701105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.740713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

59
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:19.790713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.832616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

60
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3527 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03527
97.9%
1.074
 
2.1%

Length

2025-12-31T03:46:19.886797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:19.929986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03527
97.9%
1.074
 
2.1%

Most occurring characters

ValueCountFrequency (%)
07128
66.0%
.3601
33.3%
174
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07128
66.0%
.3601
33.3%
174
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07128
66.0%
.3601
33.3%
174
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07128
66.0%
.3601
33.3%
174
 
0.7%

61
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:19.980716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.023136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

62
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3501 
1.0
 
100

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03501
97.2%
1.0100
 
2.8%

Length

2025-12-31T03:46:20.073847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.114330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03501
97.2%
1.0100
 
2.8%

Most occurring characters

ValueCountFrequency (%)
07102
65.7%
.3601
33.3%
1100
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07102
65.7%
.3601
33.3%
1100
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07102
65.7%
.3601
33.3%
1100
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07102
65.7%
.3601
33.3%
1100
 
0.9%

63
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:20.165102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.207393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

64
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:20.261169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.304423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

65
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3579 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Length

2025-12-31T03:46:20.358080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.401224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

66
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:20.454681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.499285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

67
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:20.556359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.600541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

68
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:20.653362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.695650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

69
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3578 
1.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03578
99.4%
1.023
 
0.6%

Length

2025-12-31T03:46:20.749533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.794119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03578
99.4%
1.023
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07179
66.5%
.3601
33.3%
123
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07179
66.5%
.3601
33.3%
123
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07179
66.5%
.3601
33.3%
123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07179
66.5%
.3601
33.3%
123
 
0.2%

70
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3577 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03577
99.3%
1.024
 
0.7%

Length

2025-12-31T03:46:20.845250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.886344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03577
99.3%
1.024
 
0.7%

Most occurring characters

ValueCountFrequency (%)
07178
66.4%
.3601
33.3%
124
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07178
66.4%
.3601
33.3%
124
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07178
66.4%
.3601
33.3%
124
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07178
66.4%
.3601
33.3%
124
 
0.2%

71
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:20.935279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:20.975378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

72
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:21.026044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.073795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

73
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3594 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Length

2025-12-31T03:46:21.132079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.175322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

74
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:21.231276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.282561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

75
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3591 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Length

2025-12-31T03:46:21.343784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.396547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

76
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3517 
1.0
 
84

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03517
97.7%
1.084
 
2.3%

Length

2025-12-31T03:46:21.464252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.515644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03517
97.7%
1.084
 
2.3%

Most occurring characters

ValueCountFrequency (%)
07118
65.9%
.3601
33.3%
184
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07118
65.9%
.3601
33.3%
184
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07118
65.9%
.3601
33.3%
184
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07118
65.9%
.3601
33.3%
184
 
0.8%

77
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:21.585032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.639990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

78
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:21.701540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.751971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

79
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3559 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03559
98.8%
1.042
 
1.2%

Length

2025-12-31T03:46:21.812347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.863218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03559
98.8%
1.042
 
1.2%

Most occurring characters

ValueCountFrequency (%)
07160
66.3%
.3601
33.3%
142
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07160
66.3%
.3601
33.3%
142
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07160
66.3%
.3601
33.3%
142
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07160
66.3%
.3601
33.3%
142
 
0.4%

80
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3562 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03562
98.9%
1.039
 
1.1%

Length

2025-12-31T03:46:21.923538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:21.975120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03562
98.9%
1.039
 
1.1%

Most occurring characters

ValueCountFrequency (%)
07163
66.3%
.3601
33.3%
139
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07163
66.3%
.3601
33.3%
139
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07163
66.3%
.3601
33.3%
139
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07163
66.3%
.3601
33.3%
139
 
0.4%

81
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3456 
1.0
 
145

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03456
96.0%
1.0145
 
4.0%

Length

2025-12-31T03:46:22.036038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.081017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03456
96.0%
1.0145
 
4.0%

Most occurring characters

ValueCountFrequency (%)
07057
65.3%
.3601
33.3%
1145
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07057
65.3%
.3601
33.3%
1145
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07057
65.3%
.3601
33.3%
1145
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07057
65.3%
.3601
33.3%
1145
 
1.3%

82
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:22.136048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.176731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

83
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:22.226161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.267918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

84
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3543 
1.0
 
58

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03543
98.4%
1.058
 
1.6%

Length

2025-12-31T03:46:22.318023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.358610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03543
98.4%
1.058
 
1.6%

Most occurring characters

ValueCountFrequency (%)
07144
66.1%
.3601
33.3%
158
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07144
66.1%
.3601
33.3%
158
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07144
66.1%
.3601
33.3%
158
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07144
66.1%
.3601
33.3%
158
 
0.5%

85
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:22.408858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.450575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

86
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3590 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Length

2025-12-31T03:46:22.501844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.542106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

87
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3588 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Length

2025-12-31T03:46:22.592813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.639968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

88
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3568 
1.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03568
99.1%
1.033
 
0.9%

Length

2025-12-31T03:46:22.699389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.748402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03568
99.1%
1.033
 
0.9%

Most occurring characters

ValueCountFrequency (%)
07169
66.4%
.3601
33.3%
133
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07169
66.4%
.3601
33.3%
133
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07169
66.4%
.3601
33.3%
133
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07169
66.4%
.3601
33.3%
133
 
0.3%

89
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3575 
1.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03575
99.3%
1.026
 
0.7%

Length

2025-12-31T03:46:22.806962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.855355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03575
99.3%
1.026
 
0.7%

Most occurring characters

ValueCountFrequency (%)
07176
66.4%
.3601
33.3%
126
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07176
66.4%
.3601
33.3%
126
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07176
66.4%
.3601
33.3%
126
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07176
66.4%
.3601
33.3%
126
 
0.2%

90
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3593 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Length

2025-12-31T03:46:22.916742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:22.965770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

91
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:23.029726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:23.080398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

92
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:23.149617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:23.662152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

93
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:23.716125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:23.764268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

94
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:23.825672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:23.874244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

95
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:23.934451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:23.984263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

96
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:24.046697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.095709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

97
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:24.157515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.209019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

98
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3504 
1.0
 
97

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03504
97.3%
1.097
 
2.7%

Length

2025-12-31T03:46:24.268902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.318192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03504
97.3%
1.097
 
2.7%

Most occurring characters

ValueCountFrequency (%)
07105
65.8%
.3601
33.3%
197
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07105
65.8%
.3601
33.3%
197
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07105
65.8%
.3601
33.3%
197
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07105
65.8%
.3601
33.3%
197
 
0.9%

99
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3510 
1.0
 
91

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03510
97.5%
1.091
 
2.5%

Length

2025-12-31T03:46:24.379416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.429364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03510
97.5%
1.091
 
2.5%

Most occurring characters

ValueCountFrequency (%)
07111
65.8%
.3601
33.3%
191
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07111
65.8%
.3601
33.3%
191
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07111
65.8%
.3601
33.3%
191
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07111
65.8%
.3601
33.3%
191
 
0.8%

100
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:24.491327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.541221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

101
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:24.603477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.654966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

102
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3571 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03571
99.2%
1.030
 
0.8%

Length

2025-12-31T03:46:24.718946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.769514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03571
99.2%
1.030
 
0.8%

Most occurring characters

ValueCountFrequency (%)
07172
66.4%
.3601
33.3%
130
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07172
66.4%
.3601
33.3%
130
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07172
66.4%
.3601
33.3%
130
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07172
66.4%
.3601
33.3%
130
 
0.3%

103
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:24.829735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.878607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

104
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:24.940875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:24.990701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

105
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3558 
1.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03558
98.8%
1.043
 
1.2%

Length

2025-12-31T03:46:25.052952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.104849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03558
98.8%
1.043
 
1.2%

Most occurring characters

ValueCountFrequency (%)
07159
66.3%
.3601
33.3%
143
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07159
66.3%
.3601
33.3%
143
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07159
66.3%
.3601
33.3%
143
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07159
66.3%
.3601
33.3%
143
 
0.4%

106
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:25.176908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.232154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

107
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:25.293338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.345202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

108
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:25.405057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.455023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

109
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:25.521119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.570211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

110
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:25.632496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.683032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

111
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:25.743410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.792775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

112
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:25.849565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:25.896439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

113
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:25.955959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.004491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

114
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:26.063869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.112546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

115
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:26.169567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.219192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

116
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:26.278222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.326196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

117
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:26.386210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.435254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

118
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3572 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Length

2025-12-31T03:46:26.493763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.542062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Most occurring characters

ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

119
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3561 
1.0
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03561
98.9%
1.040
 
1.1%

Length

2025-12-31T03:46:26.600956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.648412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03561
98.9%
1.040
 
1.1%

Most occurring characters

ValueCountFrequency (%)
07162
66.3%
.3601
33.3%
140
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07162
66.3%
.3601
33.3%
140
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07162
66.3%
.3601
33.3%
140
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07162
66.3%
.3601
33.3%
140
 
0.4%

120
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3594 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Length

2025-12-31T03:46:26.707907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.759528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

121
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:26.816676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.863355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

122
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:26.922132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:26.970048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

123
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:27.030123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.078165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

124
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3593 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Length

2025-12-31T03:46:27.135430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.187871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

125
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:27.252450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.313079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

126
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:27.386644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.445573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

127
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:27.516595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.598681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

128
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:27.685856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.756069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

129
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:27.836608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:27.897519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

130
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3593 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Length

2025-12-31T03:46:27.971932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.014455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03593
99.8%
1.08
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07194
66.6%
.3601
33.3%
18
 
0.1%

131
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:28.066076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.107110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

132
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:28.156643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.196373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

133
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:28.247279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.295563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

134
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3596 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Length

2025-12-31T03:46:28.349313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.391651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03596
99.9%
1.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07197
66.6%
.3601
33.3%
15
 
< 0.1%

135
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3567 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03567
99.1%
1.034
 
0.9%

Length

2025-12-31T03:46:28.442993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.484374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03567
99.1%
1.034
 
0.9%

Most occurring characters

ValueCountFrequency (%)
07168
66.4%
.3601
33.3%
134
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07168
66.4%
.3601
33.3%
134
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07168
66.4%
.3601
33.3%
134
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07168
66.4%
.3601
33.3%
134
 
0.3%

136
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:28.535470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.576264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

137
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:28.625712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.666612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

138
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:28.719474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.760509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

139
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:28.815377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.859521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

140
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:28.923470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:28.972132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

141
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:29.024400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:29.067552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

142
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:29.123299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:29.170707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

143
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:29.227216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:29.270078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

144
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3583 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03583
99.5%
1.018
 
0.5%

Length

2025-12-31T03:46:29.327296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:29.370924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03583
99.5%
1.018
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

145
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:29.425202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.059326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

146
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:30.141118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.204619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

147
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3586 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Length

2025-12-31T03:46:30.258187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.300480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

148
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3588 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Length

2025-12-31T03:46:30.374682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.422446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

149
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:30.475491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.515415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

150
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:30.567917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.609348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

151
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
2790 
1.0
811 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02790
77.5%
1.0811
 
22.5%

Length

2025-12-31T03:46:30.658634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.699717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02790
77.5%
1.0811
 
22.5%

Most occurring characters

ValueCountFrequency (%)
06391
59.2%
.3601
33.3%
1811
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06391
59.2%
.3601
33.3%
1811
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06391
59.2%
.3601
33.3%
1811
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06391
59.2%
.3601
33.3%
1811
 
7.5%

152
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3572 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Length

2025-12-31T03:46:30.750032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.791455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03572
99.2%
1.029
 
0.8%

Most occurring characters

ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07173
66.4%
.3601
33.3%
129
 
0.3%

153
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:30.844154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.887410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

154
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:30.942941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:30.984964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

155
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:31.036371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.077170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

156
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:31.127289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.168490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

157
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3553 
1.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03553
98.7%
1.048
 
1.3%

Length

2025-12-31T03:46:31.226364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.284028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03553
98.7%
1.048
 
1.3%

Most occurring characters

ValueCountFrequency (%)
07154
66.2%
.3601
33.3%
148
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07154
66.2%
.3601
33.3%
148
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07154
66.2%
.3601
33.3%
148
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07154
66.2%
.3601
33.3%
148
 
0.4%

158
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3519 
1.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03519
97.7%
1.082
 
2.3%

Length

2025-12-31T03:46:31.348193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.412145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03519
97.7%
1.082
 
2.3%

Most occurring characters

ValueCountFrequency (%)
07120
65.9%
.3601
33.3%
182
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07120
65.9%
.3601
33.3%
182
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07120
65.9%
.3601
33.3%
182
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07120
65.9%
.3601
33.3%
182
 
0.8%

159
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3589 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03589
99.7%
1.012
 
0.3%

Length

2025-12-31T03:46:31.491848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.553723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03589
99.7%
1.012
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

160
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:31.622433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.669264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

161
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:31.722257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.768814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

162
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:31.824082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.870657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

163
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3591 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Length

2025-12-31T03:46:31.923599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:31.965351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

164
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3522 
1.0
 
79

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03522
97.8%
1.079
 
2.2%

Length

2025-12-31T03:46:32.017784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.061613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03522
97.8%
1.079
 
2.2%

Most occurring characters

ValueCountFrequency (%)
07123
65.9%
.3601
33.3%
179
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07123
65.9%
.3601
33.3%
179
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07123
65.9%
.3601
33.3%
179
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07123
65.9%
.3601
33.3%
179
 
0.7%

165
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:32.116782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.163136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

166
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:32.216672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.261565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

167
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:32.316826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.359735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

168
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:32.413791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.457300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

169
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3566 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03566
99.0%
1.035
 
1.0%

Length

2025-12-31T03:46:32.508807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.552508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03566
99.0%
1.035
 
1.0%

Most occurring characters

ValueCountFrequency (%)
07167
66.3%
.3601
33.3%
135
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07167
66.3%
.3601
33.3%
135
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07167
66.3%
.3601
33.3%
135
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07167
66.3%
.3601
33.3%
135
 
0.3%

170
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3586 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Length

2025-12-31T03:46:32.603525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.645930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03586
99.6%
1.015
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07187
66.5%
.3601
33.3%
115
 
0.1%

171
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:32.696219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.738223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

172
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:32.792653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.836523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

173
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:32.893211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:32.935296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

174
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:32.988683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.034387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

175
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3579 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Length

2025-12-31T03:46:33.090029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.133327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

176
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:33.186975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.234471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

177
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:33.309115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.356896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

178
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:33.412189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.456587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

179
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:33.510038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.554114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

180
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:33.608580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.654277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

181
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:33.710136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.751339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

182
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3590 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Length

2025-12-31T03:46:33.804127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.848467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03590
99.7%
1.011
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07191
66.6%
.3601
33.3%
111
 
0.1%

183
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:33.907723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:33.955864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

184
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3588 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Length

2025-12-31T03:46:34.011738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.054796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03588
99.6%
1.013
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07189
66.5%
.3601
33.3%
113
 
0.1%

185
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.108069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.148576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

186
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.200208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.241718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

187
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3515 
1.0
 
86

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03515
97.6%
1.086
 
2.4%

Length

2025-12-31T03:46:34.298219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.352842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03515
97.6%
1.086
 
2.4%

Most occurring characters

ValueCountFrequency (%)
07116
65.9%
.3601
33.3%
186
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07116
65.9%
.3601
33.3%
186
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07116
65.9%
.3601
33.3%
186
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07116
65.9%
.3601
33.3%
186
 
0.8%

188
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.417307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.476487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

189
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:34.540144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.584784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

190
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.639295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.683092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

191
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3594 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Length

2025-12-31T03:46:34.735487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.777584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

192
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.830371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.874607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

193
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:34.928208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:34.971445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

194
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:35.024595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.065406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

195
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:35.117777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.158986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

196
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:35.209283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.254421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

197
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:35.311533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.359268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

198
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:35.419343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.469243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

199
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:35.529752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.580001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

200
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3581 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03581
99.4%
1.020
 
0.6%

Length

2025-12-31T03:46:35.635856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.682757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03581
99.4%
1.020
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07182
66.5%
.3601
33.3%
120
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07182
66.5%
.3601
33.3%
120
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07182
66.5%
.3601
33.3%
120
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07182
66.5%
.3601
33.3%
120
 
0.2%

201
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3595 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Length

2025-12-31T03:46:35.739418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.783376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03595
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07196
66.6%
.3601
33.3%
16
 
0.1%

202
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:35.838476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.880400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

203
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3579 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Length

2025-12-31T03:46:35.931955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:35.972297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03579
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07180
66.5%
.3601
33.3%
122
 
0.2%

204
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3549 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Length

2025-12-31T03:46:36.021999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.062601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Most occurring characters

ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

205
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:36.112625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.154348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

206
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3419 
1.0
 
182

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03419
94.9%
1.0182
 
5.1%

Length

2025-12-31T03:46:36.204710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.244578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03419
94.9%
1.0182
 
5.1%

Most occurring characters

ValueCountFrequency (%)
07020
65.0%
.3601
33.3%
1182
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07020
65.0%
.3601
33.3%
1182
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07020
65.0%
.3601
33.3%
1182
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07020
65.0%
.3601
33.3%
1182
 
1.7%

207
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3552 
1.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03552
98.6%
1.049
 
1.4%

Length

2025-12-31T03:46:36.294472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.334340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03552
98.6%
1.049
 
1.4%

Most occurring characters

ValueCountFrequency (%)
07153
66.2%
.3601
33.3%
149
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07153
66.2%
.3601
33.3%
149
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07153
66.2%
.3601
33.3%
149
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07153
66.2%
.3601
33.3%
149
 
0.5%

208
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3594 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Length

2025-12-31T03:46:36.383994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.424524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03594
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07195
66.6%
.3601
33.3%
17
 
0.1%

209
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3560 
1.0
 
41

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03560
98.9%
1.041
 
1.1%

Length

2025-12-31T03:46:36.474641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.518368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03560
98.9%
1.041
 
1.1%

Most occurring characters

ValueCountFrequency (%)
07161
66.3%
.3601
33.3%
141
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07161
66.3%
.3601
33.3%
141
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07161
66.3%
.3601
33.3%
141
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07161
66.3%
.3601
33.3%
141
 
0.4%

210
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3549 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Length

2025-12-31T03:46:36.567479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:36.609438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Most occurring characters

ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

211
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:37.349530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.389878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

212
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:37.440909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.483241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

213
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3525 
1.0
 
76

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03525
97.9%
1.076
 
2.1%

Length

2025-12-31T03:46:37.537590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.579904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03525
97.9%
1.076
 
2.1%

Most occurring characters

ValueCountFrequency (%)
07126
66.0%
.3601
33.3%
176
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07126
66.0%
.3601
33.3%
176
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07126
66.0%
.3601
33.3%
176
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07126
66.0%
.3601
33.3%
176
 
0.7%

214
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3580 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03580
99.4%
1.021
 
0.6%

Length

2025-12-31T03:46:37.631829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.675185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03580
99.4%
1.021
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

215
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:37.733214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.777606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

216
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:37.830534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.871987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

217
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:37.924434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:37.967774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

218
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:38.025828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.071768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

219
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:38.127602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.173599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

220
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3580 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03580
99.4%
1.021
 
0.6%

Length

2025-12-31T03:46:38.228701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.273669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03580
99.4%
1.021
 
0.6%

Most occurring characters

ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07181
66.5%
.3601
33.3%
121
 
0.2%

221
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:38.330609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.373331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

222
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:38.427970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.470992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

223
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:38.527831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.571007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

224
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:38.622440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.666264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

225
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:38.721018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.765491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

226
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3585 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03585
99.6%
1.016
 
0.4%

Length

2025-12-31T03:46:38.821201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.864980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03585
99.6%
1.016
 
0.4%

Most occurring characters

ValueCountFrequency (%)
07186
66.5%
.3601
33.3%
116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07186
66.5%
.3601
33.3%
116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07186
66.5%
.3601
33.3%
116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07186
66.5%
.3601
33.3%
116
 
0.1%

227
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3591 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Length

2025-12-31T03:46:38.920981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:38.964419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03591
99.7%
1.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07192
66.6%
.3601
33.3%
110
 
0.1%

228
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:39.017573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.062568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

229
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:39.115988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.160079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

230
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:39.214803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.259917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

231
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:39.316686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.365837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

232
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:39.421599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.467635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

233
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3514 
1.0
 
87

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03514
97.6%
1.087
 
2.4%

Length

2025-12-31T03:46:39.519002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.562264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03514
97.6%
1.087
 
2.4%

Most occurring characters

ValueCountFrequency (%)
07115
65.9%
.3601
33.3%
187
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07115
65.9%
.3601
33.3%
187
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07115
65.9%
.3601
33.3%
187
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07115
65.9%
.3601
33.3%
187
 
0.8%

234
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:39.615097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.656997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

235
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:39.708500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.752790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

236
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3476 
1.0
 
125

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03476
96.5%
1.0125
 
3.5%

Length

2025-12-31T03:46:39.808585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.849954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03476
96.5%
1.0125
 
3.5%

Most occurring characters

ValueCountFrequency (%)
07077
65.5%
.3601
33.3%
1125
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07077
65.5%
.3601
33.3%
1125
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07077
65.5%
.3601
33.3%
1125
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07077
65.5%
.3601
33.3%
1125
 
1.2%

237
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
1.0
2093 
0.0
1508 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02093
58.1%
0.01508
41.9%

Length

2025-12-31T03:46:39.900992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:39.944230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02093
58.1%
0.01508
41.9%

Most occurring characters

ValueCountFrequency (%)
05109
47.3%
.3601
33.3%
12093
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05109
47.3%
.3601
33.3%
12093
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05109
47.3%
.3601
33.3%
12093
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05109
47.3%
.3601
33.3%
12093
19.4%

238
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:39.997042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.038945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

239
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:40.091521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.132855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

240
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3597 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Length

2025-12-31T03:46:40.183154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.225079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03597
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07198
66.6%
.3601
33.3%
14
 
< 0.1%

241
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:40.276171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.319211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

242
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:40.369112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.410768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

243
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:40.462004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.503862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

244
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3582 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03582
99.5%
1.019
 
0.5%

Length

2025-12-31T03:46:40.556320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.600413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03582
99.5%
1.019
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

245
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:40.682247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.766193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

246
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3589 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03589
99.7%
1.012
 
0.3%

Length

2025-12-31T03:46:40.832131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.884276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03589
99.7%
1.012
 
0.3%

Most occurring characters

ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07190
66.6%
.3601
33.3%
112
 
0.1%

247
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:40.946902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:40.995560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

248
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:41.045949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.086717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

249
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3549 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Length

2025-12-31T03:46:41.141388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.183923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03549
98.6%
1.052
 
1.4%

Most occurring characters

ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07150
66.2%
.3601
33.3%
152
 
0.5%

250
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:41.236522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.280417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

251
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3599 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Length

2025-12-31T03:46:41.331640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.376431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03599
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07200
66.6%
.3601
33.3%
12
 
< 0.1%

252
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3375 
1.0
 
226

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03375
93.7%
1.0226
 
6.3%

Length

2025-12-31T03:46:41.428640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.468651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03375
93.7%
1.0226
 
6.3%

Most occurring characters

ValueCountFrequency (%)
06976
64.6%
.3601
33.3%
1226
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06976
64.6%
.3601
33.3%
1226
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06976
64.6%
.3601
33.3%
1226
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06976
64.6%
.3601
33.3%
1226
 
2.1%

253
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
2754 
1.0
847 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02754
76.5%
1.0847
 
23.5%

Length

2025-12-31T03:46:41.518837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.560364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02754
76.5%
1.0847
 
23.5%

Most occurring characters

ValueCountFrequency (%)
06355
58.8%
.3601
33.3%
1847
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06355
58.8%
.3601
33.3%
1847
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06355
58.8%
.3601
33.3%
1847
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06355
58.8%
.3601
33.3%
1847
 
7.8%

254
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3592 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Length

2025-12-31T03:46:41.618499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.673829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03592
99.8%
1.09
 
0.2%

Most occurring characters

ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07193
66.6%
.3601
33.3%
19
 
0.1%

255
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:41.739487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.792338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

256
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3576 
1.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03576
99.3%
1.025
 
0.7%

Length

2025-12-31T03:46:41.862622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:41.944121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03576
99.3%
1.025
 
0.7%

Most occurring characters

ValueCountFrequency (%)
07177
66.4%
.3601
33.3%
125
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07177
66.4%
.3601
33.3%
125
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07177
66.4%
.3601
33.3%
125
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07177
66.4%
.3601
33.3%
125
 
0.2%

257
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:42.009137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.051873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

258
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3598 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Length

2025-12-31T03:46:42.104889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.147524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03598
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07199
66.6%
.3601
33.3%
13
 
< 0.1%

259
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:42.196373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.238871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

260
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3600 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Length

2025-12-31T03:46:42.288671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.330599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03600
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07201
66.7%
.3601
33.3%
11
 
< 0.1%

261
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3583 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03583
99.5%
1.018
 
0.5%

Length

2025-12-31T03:46:42.382264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.423152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03583
99.5%
1.018
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07184
66.5%
.3601
33.3%
118
 
0.2%

262
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
3582 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03582
99.5%
1.019
 
0.5%

Length

2025-12-31T03:46:42.473963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.514702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03582
99.5%
1.019
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07183
66.5%
.3601
33.3%
119
 
0.2%

263
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0.0
2427 
1.0
1174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.02427
67.4%
1.01174
32.6%

Length

2025-12-31T03:46:42.565809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-31T03:46:42.608647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02427
67.4%
1.01174
32.6%

Most occurring characters

ValueCountFrequency (%)
06028
55.8%
.3601
33.3%
11174
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06028
55.8%
.3601
33.3%
11174
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06028
55.8%
.3601
33.3%
11174
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06028
55.8%
.3601
33.3%
11174
 
10.9%

Interactions

2025-12-31T03:46:10.859543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.475787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.674718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.921368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.553024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.730370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.981555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.613080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T03:46:10.795546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-31T03:46:42.943920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
0110100101102103104105106107108109111101111121131141151161171181191212012112212312412512612712812913130131132133134135136137138139141401411421431441451461471481491515015115215315415515615715815916160161162163164165166167168169171701711721731741751761771781791818018118218318418518618718818919190191192193194195196197198199220200201202203204205206207208209212102112122132142152162172182192222022122222322422522622722822923230231232233234235236237238239242402412422432442452462472482492525025125225325425525625725825926260261262263272829330313233343536373839440414243444546474849550515253545556575859660616263646566676869770717273747576777879880818283848586878889990919293949596979899
01.0000.5540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1550.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
10.5541.0000.2980.0160.0000.0440.0000.0160.0850.0650.0000.0290.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0790.1230.0250.0200.0860.0000.0000.0610.0000.0360.0000.0220.0000.0000.0460.0000.0000.0000.0150.0790.0000.0000.1260.0000.0490.0000.0510.0000.0000.0000.0000.0000.0000.0440.0000.0000.0250.3030.0130.0000.0000.0000.0000.0000.1020.0000.0470.0000.0000.0000.0330.0520.0000.0160.0000.2150.0220.0420.0580.0000.0000.0000.0470.0840.0000.0000.0330.0000.0000.0470.0000.0000.0030.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0160.0000.0000.0160.0700.2090.0280.3890.0000.0440.1220.0000.0460.0530.1700.0770.0700.0250.0320.0000.0000.0000.0700.0490.0000.0000.0000.0000.0300.0000.0110.1290.0000.2980.0000.0000.0000.0930.0410.0160.0000.0000.0000.0000.0000.0720.0000.0000.1070.2620.0000.0000.0000.0380.0000.0000.0000.0440.0000.0000.2090.0000.2490.0580.0000.0000.2290.3690.0160.0000.1310.0000.0640.0700.0250.0000.0730.0670.1880.0000.0000.1110.0330.1020.0380.0640.0000.0000.0700.0000.0130.0000.0380.0000.0000.0000.0460.1160.0000.0000.0000.0000.0990.0000.0000.0120.0000.0160.0000.0000.0000.0880.0620.0000.0000.0000.0420.0100.1370.0000.0000.0450.0000.0160.0000.0000.0000.0690.0000.0160.0240.0000.0970.0990.0000.0250.0000.0000.0970.1460.0000.0000.1830.0000.0950.0750.0000.0410.0000.0210.0000.0000.0000.0000.0160.1000.0000.0530.127
100.0000.2981.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1000.0000.0160.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1010.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1020.0000.0440.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
950.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
960.0000.1000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
980.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0210.1830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.2450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.013
990.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1520.0000.0000.0200.1870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.2210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0131.000

Missing values

2025-12-31T03:46:11.875324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-31T03:46:12.789962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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0-0.0176630.6042481.9115230.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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3596-0.0306080.6042480.8025500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3597-0.033885-0.1992590.8025500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.0
3598-0.036425-0.1992590.8025500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3599-0.041423-1.002767-0.3064230.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.0
36000.003393-0.199259-0.3064230.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.0

Duplicate rows

Most frequently occurring

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135-0.039784-1.002767-0.3064230.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.043
111-0.040112-1.002767-0.3064230.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.042
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